[](https://pipekit.io)
[Pipekit](https://pipekit.io) allows you to manage your workflows at scale. The control plane configures Argo Workflows for you in your infrastructure, enabling you to optimize multi-cluster workloads while reducing your cloud spend. The team at Pipekit is also happy to support you through your Argo Workflows journey via commercial support.
# Pipekit Python SDK
## Installation
```bash
pip install pipekit-sdk
```
## Usage
```python
from hera.workflows import (
DAG,
Parameter,
Script,
Workflow,
)
from pipekit_sdk.service import PipekitService
# Create a Pipekit service that is used to talk to the Pipekit API
pipekit = PipekitService(token="<Bearer Token>")
# List clusters and Pipes
clusters = pipekit.list_clusters()
pipes = pipekit.list_pipes()
# Create a Workflow and submit it to Pipekit.
# Hera's dag-diamond example
def my_print_script(message):
print(message)
def get_script(callable):
# Note that we have the _option_ to set `inputs=Parameter(name="message")`, but
# Hera infers the Parameters that are necessary based on the passed-in callable.
return Script(
name=callable.__name__.replace("_", "-"),
source=callable,
add_cwd_to_sys_path=False,
image="python:alpine3.6",
)
with Workflow(
generate_name="dag-diamond-",
entrypoint="diamond",
namespace="default",
) as w:
echo = get_script(my_print_script)
with DAG(name="diamond"):
A = echo(name="A", arguments=[Parameter(name="message", value="A")])
B = echo(name="B", arguments=[Parameter(name="message", value="B")])
C = echo(name="C", arguments=[Parameter(name="message", value="C")])
D = echo(name="D", arguments=[Parameter(name="message", value="D")])
A >> [B, C] >> D
# Submit the Workflow to Pipekit
pipe_run = pipekit.submit(w, "<cluster-name>")
# Tail the logs
pipekit.print_logs(pipe_run["uuid"])
```
## Further help
Please refer to the [Pipekit Documentation](https://docs.pipekit.io) for more information.
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"description": "[](https://pipekit.io)\n\n[Pipekit](https://pipekit.io) allows you to manage your workflows at scale. The control plane configures Argo Workflows for you in your infrastructure, enabling you to optimize multi-cluster workloads while reducing your cloud spend. The team at Pipekit is also happy to support you through your Argo Workflows journey via commercial support.\n\n# Pipekit Python SDK\n\n## Installation\n\n```bash\npip install pipekit-sdk\n```\n\n## Usage\n\n```python\nfrom hera.workflows import (\n DAG,\n Parameter,\n Script,\n Workflow,\n)\n\nfrom pipekit_sdk.service import PipekitService\n\n# Create a Pipekit service that is used to talk to the Pipekit API\npipekit = PipekitService(token=\"<Bearer Token>\")\n\n# List clusters and Pipes\nclusters = pipekit.list_clusters()\npipes = pipekit.list_pipes()\n\n# Create a Workflow and submit it to Pipekit.\n# Hera's dag-diamond example\ndef my_print_script(message):\n print(message)\n\ndef get_script(callable):\n # Note that we have the _option_ to set `inputs=Parameter(name=\"message\")`, but\n # Hera infers the Parameters that are necessary based on the passed-in callable.\n return Script(\n name=callable.__name__.replace(\"_\", \"-\"),\n source=callable,\n add_cwd_to_sys_path=False,\n image=\"python:alpine3.6\",\n )\n\nwith Workflow(\n generate_name=\"dag-diamond-\",\n entrypoint=\"diamond\",\n namespace=\"default\",\n) as w:\n echo = get_script(my_print_script)\n\n with DAG(name=\"diamond\"):\n A = echo(name=\"A\", arguments=[Parameter(name=\"message\", value=\"A\")])\n B = echo(name=\"B\", arguments=[Parameter(name=\"message\", value=\"B\")])\n C = echo(name=\"C\", arguments=[Parameter(name=\"message\", value=\"C\")])\n D = echo(name=\"D\", arguments=[Parameter(name=\"message\", value=\"D\")])\n A >> [B, C] >> D\n\n# Submit the Workflow to Pipekit\npipe_run = pipekit.submit(w, \"<cluster-name>\")\n\n# Tail the logs\npipekit.print_logs(pipe_run[\"uuid\"])\n```\n\n## Further help\nPlease refer to the [Pipekit Documentation](https://docs.pipekit.io) for more information.\n\n",
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